l_1-norm quantile regression screening rule via the dual circumscribed sphere
نویسندگان
چکیده
منابع مشابه
L1-Norm Quantile Regression
Classical regression methods have focused mainly on estimating conditional mean functions. In recent years, however, quantile regression has emerged as a comprehensive approach to the statistical analysis of response models. In this article we consider the L1-norm (LASSO) regularized quantile regression (L1-norm QR), which uses the sum of the absolute values of the coefficients as the penalty. ...
متن کاملRobust Multiview Registration of 3D Surfaces via L_1-norm Minimization
In this paper we present a robust method for simultaneous registration of multiple 3D scans. Rigid registration is an important task in many applications such as surface reconstruction, navigation and computer aided design. The goal of 3-D (rigid) registration is to align surfaces through a (rigid) transformation. A large number of existing registration algorithms are dependent on finding match...
متن کاملLeast quantile regression via modern optimization
The MIT Faculty has made this article openly available. Please share how this access benefits you. Your story matters. We address the Least Quantile of Squares (LQS) (and in particular the Least Median of Squares) regression problem using modern optimization methods. We propose a Mixed Integer Optimization (MIO) formulation of the LQS problem which allows us to find a provably global optimal so...
متن کاملNonparametric M-quantile Regression via Penalized Splines
Quantile regression investigates the conditional quantile functions of a response variables in terms of a set of covariates. Mquantile regression extends this idea by a “quantile-like” generalization of regression based on influence functions. In this work we extend it to nonparametric regression, in the sense that the M-quantile regression functions do not have to be assumed to be linear, but ...
متن کاملEXTREMAL QUANTILE REGRESSION 3 quantile regression
Quantile regression is an important tool for estimation of conditional quantiles of a response Y given a vector of covariates X. It can be used to measure the effect of covariates not only in the center of a distribution, but also in the upper and lower tails. This paper develops a theory of quantile regression in the tails. Specifically , it obtains the large sample properties of extremal (ext...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Transactions on Pattern Analysis and Machine Intelligence
سال: 2021
ISSN: 0162-8828,2160-9292,1939-3539
DOI: 10.1109/tpami.2021.3087160